Location: Bengaluru, Karnataka, India

Promote design and delivery of agentic AI that improves Software quality, security and resilience in regulated settings

As an Applied AI ML Lead within JP Morgan Chase Asset & Wealth Management Technology, you will lead the design and delivery of agentic AI solutions that improve software quality, security, and resiliency in a regulated environment. You will build and scale autonomous agents that detect and auto-remediate code and infrastructure definitions that fall short of wellarchitected principles and firm engineering governance standards spanning the inner loop (IDE-time) and the outer loop (CI/CD pipeline-time compensating controls that prevent non-compliant changes from reaching production).

Job responsibilities

• Own end-to-end technical direction for agentic capabilities: architecture, delivery plan, reliability, security, and adoption.
• Design and implement LLM-driven agents for code generation/refactoring, standards conformance, test creation, documentation updates, release readiness checks, and operational insights.
• Establish safe auto-heal patterns: diff/PR-based remediation, risk-tiered actions, human-in-the-loop approvals, and explainable decisions.
• Build orchestration and coordination for multi-agent workflows (e.g., LangGraph / AutoGen or similar): state management, tool-calling, structured outputs, and guardrails.
• Implement outer-loop pipeline agent stages as compensating controls: policy checks, risk scoring, exception routing, evidence collection, and release gating.
• Define and run a rigorous evaluation program: regression suites, golden datasets, adversarial testing, prompt/model versioning, rollout controls, and continuous monitoring.
• Partner with governance, security, platform engineering, and application teams to translate standards into enforceable automation and measurable outcomes.
• Mentor and raise the bar for engineering quality through design reviews, coaching, and setting team best practices.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience

• Significant software engineering experience with proven technical leadership delivering production systems at scale.
• Demonstrated experience building/shipping LLM-enabled applications (agents/tool use, structured outputs/validation, grounding/RAG, observability).
• Strong SDLC understanding across IDE/inner loop, PR workflows, and CI/CD/outer loop in regulated environments.
• Security-first engineering mindset: least privilege, secrets hygiene, auditability/traceability, change controls, and secure-by-design automation.
• Excellent stakeholder communication; ability to drive alignment across engineering, product, security, and governance.

Preferred qualifications, capabilities, and skills

• Python (FastAPI/Pydantic) and strong distributed systems/reliability background.
• Experience building automated remediation (codemods/refactoring tools) and policy/guardrail systems with explain ability.
• Strong observability discipline (logs/metrics/tracing; OpenTelemetry and common monitoring platforms).